# SIMPLE: Simulation-Based Policy Learning and Evaluation for Humanoid Loco-manipulation

arXiv: https://arxiv.org/abs/2606.08278 · HTML: https://mlsanity.com/paper/2606.08278/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2026.

## Built on by (most cited first, 8 of 8)

- [WholeBodyWAM: Generalizing Pre-trained World-Action Priors to Humanoid Loco-Manipulation via WBC-Grounded Coordination](https://mlsanity.com/paper/2609.16644/) (2026)
- [Beyond Kinematics: Benchmarking Simulation Fidelity for Muscle-Driven Imitation Learning](https://mlsanity.com/paper/2609.21909/) (2026)
- [CoHuB: A Simulation Benchmark for Multi-Humanoid Collaboration](https://mlsanity.com/paper/2609.34782/) (2026)
- [Humanoid Loco-Manipulation With Discrete VLA Model](https://mlsanity.com/paper/2609.35709/) (2026)
- [HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution](https://mlsanity.com/paper/2610.02089/) (2026)
- [BiGym 2.0: Benchmarking Learned and Agent-Developed Policies for Humanoid Household Manipulation](https://mlsanity.com/paper/2610.07594/) (2026)
- [Being-M0.7: A Latent World-Action Model for Humanoid Robots](https://mlsanity.com/paper/2610.11283/) (2026)
- [LIVIN: Benchmarking Spatial and Embodied Intelligence in Digital Twins of Lived-In Homes](https://mlsanity.com/paper/2610.12069/) (2026)

## Abstract (arXiv)

Humanoid foundation models are advancing faster than we can evaluate them. While real-world testing is expensive and difficult to reproduce, existing simulation benchmarks focus primarily on table-top or wheeled robots. A scalable and reproducible benchmark for whole-body humanoid loco-manipulation remains an open problem. To this end, we present SIMPLE, a unified simulation testbed for humanoid policy learning and evaluation. SIMPLE couples the accurate contact-rich dynamics of MuJoCo with the photorealistic rendering of IsaacSim. It provides a large-scale environment comprising 16 diverse whole-body tasks, 50 indoor scenes, and over 1,000 object assets. To facilitate scalable data collection, the framework integrates two data generation pipelines: automated trajectory generation via motion planning and a low-latency VR teleoperation interface. We further integrate and benchmark mainstream humanoid policies at scale in SIMPLE, including lightweight imitation networks, large vision-language-action (VLA) models, and recent world action models (WAMs). Our experiments reveal a strong correlation between policy performance in simulation and the real world. Furthermore, we demonstrate that policies trained on data collected in SIMPLE can be transferred zero-shot to physical humanoid robots under similar settings, providing a robust and reproducible foundation for humanoid robotics research.